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Southern Andean glaciers contribute substantially to global sea-level rise. Unfortunately, mass balance estimates prior to 2000 are limited, hindering our understanding of the evolution of glacier mass changes over time. Elevation changes over 1976/1979 to 2000 derived from historical KH-9 Hexagon imagery and NASADEM provide the basis for geodetic mass balance estimates for subsets of the Northern Patagonian Icefield (NPI) and the Southern Patagonian Icefield (SPI), extending current mass balance observations by ∼20 years. Geodetic mass balances were −0.63 ± 0.03 m w.e. yr −1 for 63% of the NPI and −0.33 ± 0.05 m w.e. yr −1 for 52% of the SPI glacierized areas for this historical period. We also extend previous estimates temporally by 25% using NASADEM and ASTER elevation trends for the period 2000 to 2020, and find geodetic mass balances of −0.86 ± 0.03 m w.e. yr −1 for 100% of the NPI and −1.23 ± 0.04 m w.e. yr −1 for 97% of the SPI glacierized areas. 2000–2020 aggregations for the same areas represented in the 1976/1979 to 2000 estimates are −0.78 ± 0.03 m w.e. yr −1 in the NPI and −0.80 ± 0.04 m w.e. yr −1 on the SPI. The significant difference in SPI geodetic mass balance in the modern period for 100% vs. 52% of the glacierized area suggests subsampling leads to significant biases in regional mass balance estimates. When we compare the same areas in each time period, the results highlight an acceleration of ice loss by a factor of 1.2 on the NPI and 2.4 on the SPI in the 21st century as compared to the 1976/1979 to 2000 period. While lake-terminating glaciers show the most significant increase in mass loss rate from 1976/1979–2000 to 2000–2020, mass balance trends are highly variable within glaciers of all terminus environments, which suggests that individual glacier sensitivity to climate change is dependent on a multitude of morphological and climatological factors. 

Pakistan is the most glaciated country on the planet but faces increasing water scarcity due to the vulnerability of its primary water source, the Indus River, to changes in climate and demand. Glacier melt constitutes over one-third of the Indus River’s discharge, but the impacts of glacier shrinkage from anthropogenic climate change are not equal across all eleven subbasins of the Upper Indus. We present an exploration of glacier melt contribution to Indus River flow at the subbasin scale using a distributed surface energy and mass balance model run 2001–2013 and calibrated with geodetic mass balance data. We find that the northern subbasins, the three in the Karakoram Range, contribute more glacier meltwater than the other basins combined. While glacier melt discharge tends to be large where there are more glaciers, our modeling study reveals that glacier melt does not scale directly with glaciated area. The largest volume of glacier melt comes from the Gilgit/Hunza subbasin, whose glaciers are at lower elevations than the other Karakoram subbasins. Regional application of the model allows an assessment of the dominant drivers of melt and their spatial distributions. Melt energy in the Nubra/Shyok and neighboring Zaskar subbasins is dominated by radiative fluxes, while turbulent fluxes dominate the melt signal in the west and south. This study provides a theoretical exploration of the spatial patterns to glacier melt in the Upper Indus Basin, a critical foundation for understanding when glaciers melt, information that can inform projections of water supply and scarcity in Pakistan. 

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.