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19 results for “glacier mass balance”
Summarized glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package contains total mass balance changes at each stake measured on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. These values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and snow densities). Included here for each stake is the total water equivalent mass change. The standard deviation or the range for each total is given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Snow, ice, and total glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes mass balance changes at each stake on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. The values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and glacier snow densities). Included here for each stake are the change in ice and snow water equivalent (mass) values, and the total mass change. The standard deviation or the range for each total is also given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Arctic glaciers mass balance from satellite gravimetry only
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry only. A similar dataset using an a priori based on DEM differencing has been submitted to GLAMBIE (Pfeffer et al., 2024). previously. The dataset submitted here does not use an a priori, but uses only satellite gravimetry data. </p> <p>The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory , namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and, Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated from Blazquez et al. (2018) is adopted to evaluate uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. The effect of land hydrology is estimated for each region, but not corrected in the total mass balance dataset, because of the small water mass balance values and large errors inherent to hydrological models. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridation<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023).</p> <p><strong>Reference</strong>:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Pfeffer, J., Coupry, B., Berthier, E., Blazquez, A., & Barnoud, A. (2024). Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing [Jeu de données]. Zenodo. https://doi.org/10.5281/ZENODO.13134559</li> </ul> <p> </p>
Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On"
<p>Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On" Data" </p>
COSIPY distributed simulations of Mera Glacier mass and energy balance (20161101-20201101)
<p>The four netCDF files contain outputs from COSIPY model (Sauter et al., 2020) for Mera Glacier for the period 20161101 to 20201101. The model is run on a 0.003°*0.003° grid, and forced with meteological variables collected locally and distributed with constant gradients. The "constants.py" is the python file that contains the specific model settings.</p>
Reconstructed, Long-Term Meteorological Forcing and Mass Balance over Glaciers in High Mountain Asia
<p>%%%% Reconstructed_WRF_Forcing_HMA.mat %%%%%%%%<br> A reconstructed, long-term data series of meteorological data for three glaciers in distinct regions of High Mountain Asia. The meteorological forcing consists of hourly Weather Research and Forecasting (WRF) data bias-corrected by high elevation, off-glacier automatic weather station (AWS) data. The variables of the forcing data consist of 2m air temperature ('T' °C), relative humidity ('RH' %), air pressure ('PRESS' hPa), incoming shortwave ('SWIN' Wm-2) and longwave ('LWIN' Wm-2) radiation, precipitation ('PP', mm hr) and wind speed ('FF' m s-1). The period of the timeseries ranges from January 1981 to December 2019. <br> <br> Data are available for the following glaciers (bias-corrected to the given coordinates / elevation of the off-glacier AWS)<br> Yala Glacier, Nepal (28.237°N 85.619°E, 5090 m a.s.l.) - 'AWS Yala Basecamp' (AWS Data available on the ICIMOD RDS)<br> Parlung Glacier Number 4 (29.245°N 96.928°E, 4600 m a.s.l.) - 'AWS4600' (Contact author Wei Yang for AWS data requests)</p> <p>Mugagangqiong Glacier (32.234°N, 87.485°E, 5850 m a.s.l.) - 'AWS5850' (Contact author Wei Yang for AWS data requests)</p> <p> </p> <p>Data are stored as matlab tables in .mat files. Data were created using Matlab version 2020b. </p> <p> </p> <p>%%%% Reconstructed_Mass_Balance_HMA.mat %%%%%%%%<br> <br> Reconstructed cumulative mass balances for the aforementioned glaciers (values in m w.e.)</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data products from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence"
<p>GNSS, GNSS-IR, and mass balance data from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence". Contains the following folders and files</p> <ul> <li>GNSS <ul> <li><em>Precise point positioning solution (.pos, etc) using the CSRS-PPP tool for each GNSS system</em></li> </ul> </li> <li>GNSS_basefix <ul> <li><em>Precise point positioning solution (.pos) with base station observations using the Emlid Studio desktop application for GNSS systems AB floating and AB fixed</em></li> </ul> </li> <li>GNSSIR<br> <ul> <li><em>Reflector height solutions for site AB floating, AB fixed, and D floating (see Fig. 7). The filename is the day of year 2023.</em></li> </ul> </li> <li>Monitored Ablation Stake<br> <ul> <li><em>Processed daily and seasonal climatic mass balance height changes (see Fig. 5)</em></li> </ul> </li> </ul>
Geodetic mass balance of glaciers in the Manaslu region of Nepal 1970 - 2013
<p>Dataset of the geodetic mass balance and surface elevation changes for the period 1970-2013 for glaciers in the Manaslu area of the Nepal Himalaya. Mean geodetic mass balances are computed from a 1970 DEM generated from CoronaKH4 imagery and 2013 HMA DEM elevations. Analysis was done at 8m spatial resolution.</p> <p>Data formats:</p> <p>Data are provided in two formats:</p> <p>1) shapefile format showing the 1970 glacier outlines for 136 glaciers with valid coverage with a GLIMS ID and mean geodetic mass balance attributes, structured as follows:</p> <p>[POINT_X]: Longitude of the glacier polygon centroid</p> <p>[POINT_Y]: Latitude of the glacier polygon centroid</p> <p>[GLIMSID]: ID of each polygon, generated from the lat long coordinates as per GLIMS standard guidelines</p> <p>[src_image]: image source</p> <p>[src_date]: date of the imagery</p> <p>dh_ma-1: surface elevation change [meters per year]</p> <p>bn_m_weq: geodetic mass balance [ m water equivalent per year]</p> <p>err_dh_ma-1: error term for surface elevation change [meters per year]</p> <p>err_bn : error term for the geodetic mass balance [meters water equivalent per year]</p> <p>area_m2: area of each glacier polygon in m2</p> <p>area_km2: area of each polygon in km2</p> <p>line_type: types of glacier (clean/debris covered)</p> <p>analysis_yr: year of analysis</p> <p> </p> <p>2) excel file containing glacier GLIMS ID, mean elevation change and mean mass balance</p> <p>Coordinate system: UTM datum WGS84 zone 45N</p>
Derived products from "Beyond glacier wide mass balances: parsing seasonal elevation change into spatially-resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska"
<p>Distributed mass balances, emergence velocities, firn compaction rates, and co-registered 10 m resolution DEMs from "Beyond glacier wide mass balances: parsing seasonal elevation change into spatially-resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska" (in review) (Zeller et al., 2022).</p>
Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry and DEM (Digital Elevation Model) differencing. The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and the Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated by Blazquez et al. (2018) is adopted to assess uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. A priori information from DEM differencing (Hugonnet et al., 2021) is used to reduce leakage errors associated with the mislocation of signal sources. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>References:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., & Kääb, A. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856), Article 7856. https://doi.org/10.1038/s41586-021-03436-z</li> </ul> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridization<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023). </p>
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier surface mass balance (SMB) data are crucial to understand and quantify the regional effects of climate on<br> glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present<br> a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This<br> dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct and remote sensing<br> SMB observations, meteorological reanalyses and topographical data from glacier inventories. This data science reconstruction<br> approach is embedded as a SMB component of the open-source ALpine Parameterized Glacier Model (ALPGM: https://zenodo.org/record/3609136). An extensive cross-validation allowed to assess the method’s validity, with an estimated average error (RMSE) of 0.49 m.w.e. a<sup>-</sup><sup>1</sup>, an explained variance (r<sup>2</sup>) of 79% and an average bias of +0.017 m.w.e. a<sup>-</sup><sup>1</sup>. We estimate an average regional area-weighted glacier-wide SMB of -0.72±0.20 m.w.e. a<sup>-</sup><sup>1</sup> for the 1967-2015 period, with moderately negative mass balances in the 1970s (-0.52 m.w.e. a<sup>-</sup><sup>1</sup>) and 1980s (-0.12 m.w.e. a<sup>-</sup><sup>1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.39<br> m.w.e. a<sup>-</sup><sup>1</sup> in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for this period are the Chablais (-0.90 m.w.e. a<sup>-</sup><sup>1</sup>) and Ubaye and Champsaur (-0.91 m.w.e. a<sup>-</sup><sup>1</sup> both) ranges, and the ones presenting the lowest mass losses are the Mont-Blanc and Oisans ranges (-0.74 and -0.78 m.w.e. a<sup>-</sup><sup>1</sup> respectively). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific meltwater contributions in glacierized catchments.</p> <p>The SMB dataset is comprised of multiple <em>CSV</em> files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: <em>GLIMS-ID_RGI-ID_SMB.csv</em>. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide SMB time series. Glaciers with remote sensing-derived observations (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-</sup><sup>1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;). All topographical data for the 661 glaciers can be found in the updated version of the 2003 glacier inventory included in the Supplementary material.</p>
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier mass balance (MB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct MB observations and remote sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed through an extensive cross-validation against a dataset of 32 glaciers , with an estimated average error (RMSE) of 0.55 m.w.e. a<sup>-1</sup>, an explained variance (r2) of 75% and an average bias of -0.021 m.w.e. a<sup>-1</sup>. We estimate an average regional area-weighted glacier-wide MB of -0.71±0.21 (1 sigma) m.w.e. a<sup>-1</sup> for the 1967-2015 period, with negative mass balances in the 1970s (-0.44 m.w.e. a<sup>-1</sup>), moderately negative in the 1980s (-0.16 m.w.e. a<sup>-1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.34 m.w.e. a<sup>-1</sup> in the 2010s. A comparison with ASTER-derived geodetic MB for the 2000-2015 period showed important differences with the photogrammetric geodetic MB used to train our model. When recalibrating our reconstructions with the new ASTER-derived geodetic MB, the estimated average regional area-weighted glacier-wide MB (1967-2015) is reduced to -0.64±0.21 (1 sigma) m.w.e. a<sup>-1</sup>. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967-2015 period are the Chablais (-0.93 m.w.e. a<sup>-1</sup>), Champsaur and Haute-Maurienne (-0.86 m.w.e. a<sup>-1</sup> both) and Ubaye ranges (-0.83 m.w.e. a<sup>-1</sup>), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.69 m.w.e. a<sup>-1</sup>), Oisans and Haute-Tarentaise ranges (-0.75 m.w.e. a<sup>-1</sup> both). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.</p> <p>The MB dataset is presented in two different formats: (a) A single netCDF file containing the MB reconstructions, the glacier RGI and GLIMS IDs and the glacier names. This file contains all the necessary information to correctly interact with the data, including some metadata with the authorship and data units. (b) A dataset comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide MB time series. Glaciers with remote sensing-derived estimates (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;).</p>
Caucasus glacier mass balance and thickness changes in 2000-2019
<p>In this study, we used glacier mass balance and thickness data obtained from geodetic method by Hugonnet et al. (2021) to clarify the response of region wide glacier mass balance to climate change in the Greater Caucasus over the last two decades (2000‒2019). We also analyze the long-term spatio-temporal glacier surface elevation and specific mass change for the regional, river basin, and individual glaciers. Besides, we provide a key dataset (shp file) that can be useful for further studies, including for example, hydrological modeling of runoff changes, as well as for the implementation of water management plans in this region.</p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly RACMO fields, Greenland and Antarctica 1979-2014
<p>These model output are those presented in the manuscript that provides the first description of version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly RACMO output from 1979-2014. </p>
Nonlinear sensitivity of glacier-mass balance to climate attested by temperature-index models; synthetic data
<p>Synthetic data and results of the PDD model used in the paper <a href="https://doi.org/10.5194/tc-2022-210">https://doi.org/10.5194/tc-2022-210</a></p>
Glacier Mass Balance and Regime Measurements and Analysis, 1945-2003, Version 1
This data set consists of glacier regime parameters observed between 1945 and 2003. Data include annual mass balances, ablation, accumulation, and equilibrium-line altitude of mountain and subpolar glaciers outside the two major ice sheets. All available sources of information, such as publications, archived data, and personal communications have been collected, and include time series of more than 300 glaciers. Data have been digitized and quality checked.
High Mountain Asia 2 m DEM, Surface Velocity, and Lagrangian Surface Mass Balance for Select Debris Covered Glaciers V001
This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1. This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.
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