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Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

Climate models predict amplified warming at high elevations in low latitudes, making tropical glacierized regions some of the most vulnerable hydrological systems in the world. Observations reveal decreasing streamflow due to retreating glaciers in the Andes, which hold 99 % of all tropical glaciers. However, the timescales over which meltwater contributes to streamflow and the pathways it takes – surface and subsurface – remain uncertain, hindering our ability to predict how shrinking glaciers will impact water resources. Two major contributors to this uncertainty are the sparsity of hydrologic measurements in tropical glacierized watersheds and the complication of hydrograph separation where there is year-round glacier melt. We address these challenges using a multi-method approach that employs repeat hydrochemical mixing model analysis, hydroclimatic time series analysis, and integrated watershed modeling. Each of these approaches interrogates distinct timescale relationships among meltwater, groundwater, and stream discharge. Our results challenge the commonly held conceptual model that glaciers buffer discharge variability. Instead, in a subhumid watershed on Volcán Chimborazo, Ecuador, glacier melt drives nearly all the variability in discharge (Pearson correlation coefficient of 0.89 in simulations), with glaciers contributing a broad range of 20 %–60 % or wider of discharge, mostly (86 %) through surface runoff on hourly timescales, but also through infiltration that increases annual groundwater contributions by nearly 20 %. We further found that rainfall may enhance glacier melt contributions to discharge at timescales that complement glacier melt production, possibly explaining why minimum discharge occurred at the study site during warm but dry El Niño conditions, which typically heighten melt in the Andes. Our findings caution against extrapolations from isolated measurements: stream discharge and glacier melt contributions in tropical glacierized systems can change substantially at hourly to interannual timescales, due to climatic variability and surface to subsurface flow processes.