Using an ensemble of close- and long-range remote sensing, lake bathymetry and regional meteorological data, we present a detailed assessment of the geometric changes of El Morado Glacier in the Central Andes of Chile and its adjacent proglacial lake between 1932 and 2019. Overall, the results revealed a period of marked glacier down wasting, with a mean geodetic glacier mass balance of −0.39 ± 0.15 m w.e.a−1 observed for the entire glacier between 1955 and 2015 with an area loss of 40% between 1955 and 2019. We estimate an ice elevation change of −1.00 ± 0.17 m a−1 for the glacier tongue between 1932 and 2019. The increase in the ice thinning rates and area loss during the last decade is coincident with the severe drought in this region (2010–present), which our minimal surface mass-balance model is able to reproduce. As a result of the glacier changes observed, the proglacial lake increased in area substantially between 1955 and 2019, with bathymetry data suggesting a water volume of 3.6 million m3 in 2017. This study highlights the need for further monitoring of glacierised areas in the Central Andes. Such efforts would facilitate a better understanding of the downstream impacts of glacier downwasting.

Information about end‐of‐winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high‐mountain regions. Nevertheless, such information typically relies upon extrapolation from a sparse network of observations at low elevations. Here, we test the potential of high‐resolution snow depth data derived from optical stereophotogrammetry of Pléiades satellites for improving the representation of snow depth initial conditions (SDICs) in a glacio‐hydrological model and assess potential improvements in the skill of snowmelt and streamflow simulations in a high‐elevation Andean catchment. We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground‐based and satellite observations, and consider the relative importance of accurate estimates of SDICs compared to model parameters and forcings. We find that Pléiades SDICs improve the simulation of snow‐covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs based upon extrapolation of meteorological variables or statistical methods to estimate SDICs based upon topography. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months that control albedo and melt rates over a large elevation range (>2,400 m). As such, appropriately characterizing the distribution of total snow volume with elevation is important for reproducing total streamflow and the proportions of snowmelt. Therefore, optical stereo‐photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for large spatial domains.