Glacier Index for High Mountain Asia

Zenodo (CERN European Organization for Nuclear Research) · 2026-02-18

The Glacier Index and the downscaled precipitation data from 8 different climate realizations that were used to create the Glacier Index can be found in this archive. The dataset of downscaled precipitation using GFDL-SPEAR is available at https://doi.org/10.5067/gxaa63dtmc34. The glacier data, such as the glacier mass and glacier runoff, that was used can be found at https://nsidc.org/data/hma2_ggp/versions/1. The code used to process all of this data can be found at https://github.com/mlummus/Glacier_Index. The Glacier Index and it's components can be found in the files named "gindex_all_models_scenarioSSP5-85.nc" and "gindex_all_models_scenarioSSP2-45.nc" below.

Glacier Index for High Mountain Asia

Open MIND · 2026-02-18

The Glacier Index and the downscaled precipitation data from 8 different climate realizations that were used to create the Glacier Index can be found in this archive. The dataset of downscaled precipitation using GFDL-SPEAR is available at https://doi.org/10.5067/gxaa63dtmc34. The glacier data, such as the glacier mass and glacier runoff, that was used can be found at https://nsidc.org/data/hma2_ggp/versions/1. The code used to process all of this data can be found at https://github.com/mlummus/Glacier_Index. The Glacier Index and it's components can be found in the files named "gindex_all_models_scenarioSSP5-85.nc" and "gindex_all_models_scenarioSSP2-45.nc" below.

Introducing Glaciohydrological Model Calibration Using Sentinel‐1 SAR Wet Snow Maps in the Himalaya‐Karakoram

Water Resources Research · 2025-11-25

Abstract Field‐based studies are limited in Himalaya‐Karakoram (HK); therefore, remote sensing and glaciohydrological modeling provide alternative solutions to investigate runoff evolution under changing climate conditions. Due to limited in situ runoff data in HK, glaciohydrological models are often calibrated using high‐resolution remote sensing data. This study introduces the calibration of the glaciohydrological model Spatial Processes in Hydrology (SPHY), at glacier catchment‐scale over 2000–2023 using satellite‐based Sentinel‐1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates in the HK region. The selected calibrated model parameters are validated against in situ runoff data to test the robustness of satellite‐based calibration for Chhota Shigri Glacier (CSG), Dokriani Bamak Glacier (DBG), and Gangotri Glacier System (GGS) catchments in HK. The SPHY modeled and in situ catchment‐wide runoff estimates show good agreement. The Sentinel‐1 SAR‐derived wet snow percentage area shows strong spatial and temporal variability from 2015 to 2023. The mean annual runoff is 1.79 ± 0.15 m 3 s −1 , 1.63 ± 0.09 m 3 s −1 and 39.40 ± 3.15 m 3 s −1 over 2000–2023 for CSG, DBG and GGS catchments, respectively. Maximum annual runoff occurred in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snowmelt runoff is highest in CSG (61%) and GGS (49%), while rainfall‐runoff dominates in DBG (42%). Satellite‐based glaciohydrological model calibration offers a unique opportunity to improve runoff estimates for glacierized catchments in data‐sparse regions. Applying present study to glacierized catchments lacking in situ runoff data will strengthen our past, present, and future glaciohydrological understanding of regions such as HK and Andes.

Patagonian Ice Sheet shaped regional climate during the Last Glacial Maximum

Communications Earth & Environment · 2025-10-08 · 2 citations

During the Last Glacial Maximum, changes in the thickness of the Patagonian Ice Sheet modified southern Andean topography. However, the resulting atmospheric feedbacks remain poorly constrained. Using an atmosphere–land coupled model, we isolated the climate response to prescribed ice-sheet thicknesses. Our results indicate that a thicker ice sheet generates a decrease in low level zonal winds, a westward shift in precipitation, a temperature increase along the western margin of Eastern Patagonia, and an increase in storm activity over Patagonia. A decrease in thickness generates the opposite pattern. Our findings suggest that the Patagonian Ice Sheet not only responded to climate change, but also actively modulated it—highlighting the role of topographic forcing in shaping atmospheric circulation over the Southern Hemisphere mid-latitudes. The Patagonian ice sheet not only responded to climate change but also actively influenced it regionally, highlighting the role of topographic forcing in shaping atmospheric circulation during glacial-interglacial cycles, according to a modeling study.

Brief communication: Impact of mountain glaciers on regional hydroclimate

2025-02-26 · 1 citations

Abstract. The crucial role of glaciers as a water supply underscores the need to reliably simulate alpine climate change, including glacier-atmosphere interactions. The presence of a glacier can change precipitation by generating mountain-valley scale flows, but we show here that their impacts on the atmosphere are more profound and much larger in scale. In a validated regional climate model, modest changes to the size of glacier termini in the Karakoram altered the large-scale summer monsoonal circulation, producing precipitation anomalies of sufficient magnitude and scale to overwhelm valley-scale orographic effects. Notably, the robust synoptic-scale moisture flow response exerted a substantial influence on precipitation and overwhelmed the localized response of the orographic flows, highlighting the significant impact of glacier ice on the monsoonal circulation and, hence, precipitation. These changes in turn impact glacier mass balance over the Karakoram range, emphasizing the importance of proper specification of glaciated area for the study of hydroclimate monitoring.

An ensemble machine learning approach for filling voids in surface elevation change maps over glacier surfaces

2025-11-18

Abstract. Glacier mass balance assessments in mountainous regions often rely on digital elevation models (DEMs) to estimate surface elevation change. However, these DEMs are prone to spatial data voids, particularly during historical reconstructions using older imagery. These voids, which are most common in glacier accumulation zones, introduce uncertainty into estimates of glacier mass balance and surface elevation change. Traditional void-filling methods, such as constant and hypsometric interpolation, have limitations in capturing spatial variability in elevation change. This study introduces a machine-learning- based approach using gradient-boosted tree regression (XGBoost) to estimate glacier surface-elevation change across voids. High Mountain Asia (HMA) is an ideal study area for assessing the accuracy of different void-filling approaches across glaciers with varying morphology and climatic settings. We compare XGBoost predictions to traditional void-filling methods across the Western and Eastern Himalayas using a dataset of DEM-derived elevation changes. Results indicate that XGBoost consistently outperforms simpler methods, reducing root mean square error (RMSE) and mean absolute error (MAE) while improving alignment with observed elevation changes. The study highlights the advantages of integrating multiple glaciological and topographic predictors, demonstrating the potential of machine learning to improve assessments of glacier mass balance and elevation change. Future research should explore additional predictors, such as climate data, to further enhance predictive accuracy.

Fusing Climate Data Products Using a Spatially Varying Autoencoder

Journal of Agricultural Biological and Environmental Statistics · 2024-10-22

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

Journal of Geophysical Research Atmospheres · 2024-10-26 · 4 citations

Abstract Within High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F ‐test p ‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Fusing Climate Data Products using a Spatially Varying Autoencoder

arXiv (Cornell University) · 2024-03-12

Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are placed on the autoencoder as it learns patterns in the data, creating an interpretable consensus that includes the important features from each input. We demonstrate the utility of the autoencoder by combining information from multiple precipitation products in High Mountain Asia.

Constraining Mountain Streamflow Constituents by Integrating Citizen Scientist Acquired Geochemical Samples and Sentinel‐1 SAR Wet Snow Time‐Series for the Shimshal Catchment in the Karakoram Mountains of Pakistan

Water Resources Research · 2023-02-21 · 4 citations

Abstract Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB.