We present a field‐data rich modelling analysis to reconstruct the climatic forcing, glacier response, and runoff generation from a high‐elevation catchment in central Chile over the period 2000–2015 to provide insights into the differing contributions of debris‐covered and debris‐free glaciers under current and future changing climatic conditions. Model simulations with the physically based glacio‐hydrological model TOPKAPI‐ETH reveal a period of neutral or slightly positive mass balance between 2000 and 2010, followed by a transition to increasingly large annual mass losses, associated with a recent mega drought. Mass losses commence earlier, and are more severe, for a heavily debris‐covered glacier, most likely due to its strong dependence on snow avalanche accumulation, which has declined in recent years. Catchment runoff shows a marked decreasing trend over the study period, but with high interannual variability directly linked to winter snow accumulation, and high contribution from ice melt in dry periods and drought conditions. The study demonstrates the importance of incorporating local‐scale processes such as snow avalanche accumulation and spatially variable debris thickness, in understanding the responses of different glacier types to climate change. We highlight the increased dependency of runoff from high Andean catchments on the diminishing resource of glacier ice during dry years.

Obtaining detailed information about high mountain snowpacks is often limited by insufficient ground‐based observations and uncertainty in the (re)distribution of solid precipitation. We utilize high‐resolution optical images from Pléiades satellites to generate a snow depth map, at a spatial resolution of 4 m, for a high mountain catchment of central Chile. Results are negatively biased (median difference of −0.22 m) when compared against observations from a terrestrial Light Detection And Ranging scan, though replicate general snow depth variability well. Additionally, the Pléiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40–50° (2%). We correct and filter the Pléiades snow depths using surface classification techniques of snow‐free areas and a random forest model for data gap filling. Snow depths (with an estimated error of ~0.36 m) average 1.66 m and relate well to topographical parameters such as elevation and northness in a similar way to previous studies. However, estimations of snow depth based upon topography (TOPO) or physically based modeling (DBSM) cannot resolve localized processes (i.e., avalanching or wind scouring) that are detected by Pléiades, even when forced with locally calibrated data. Comparing these alternative model approaches to corrected Pléiades snow depths reveals total snow volume differences between −28% (DBSM) and +54% (TOPO) for the catchment and large differences across most elevation bands. Pléiades represents an important contribution to understanding snow accumulation at sparsely monitored catchments, though ideally requires a careful systematic validation procedure to identify catchment‐scale biases and errors in the snow depth derivation.

ABSTRACT. The spatio-temporal distribution of air temperature over mountain glaciers can demonstrate complex patterns, yet it is often represented simplistically using linear vertical temperature gradients (VTGs) extrapolated from off-glacier locations. We analyse a network of centreline and lateral air temperature observations at Tsanteleina Glacier, Italy, during summer 2015. On average, VTGs are steep), but they are shallow under warm ambient conditions when the correlation between air temperature and elevation becomes weaker. Published along-flowline temperature distribution methods explain centreline observations well, including warming on the lower glacier tongue, but cannot estimate lateral temperature variability. Application of temperature distribution methods improves simulation of melt rates (RMSE) in an energy-balance model by up to 36% compared to the environmental lapse rate extrapolated from an off-glacier station. However, results suggest that model parameters are not easily transferable to glaciers with a small fetch without recalibration. Such methods have potential to improve estimates of temperature across a glacier, but their parameter transferability should be further linked to the glacier and atmospheric characteristics. Furthermore, 'cold spots', which can be >2°C cooler than expected for their elevation, whose occurrence is not predicted by the temperature distribution models, are identified at one-quarter of the measurement sites.

ABSTRACT. Near-surface air temperature is an important determinant of the surface energy balance of glaciers and is often represented by a constant linear temperature gradients (TGs) in models. Spatiotemporal variability in 2 m air temperature was measured across the debris-covered Miage Glacier, Italy, over an 89 d period during the 2014 ablation season using a network of 19 stations. Air temperature was found to be strongly dependent upon elevation for most stations, even under varying meteorological conditions and at different times of day, and its spatial variability was well explained by a locally derived mean linear TG (MG-TG) of −0.0088°C m −1 . However, local temperature depressions occurred over areas of very thin or patchy debris cover. The MG-TG, together with other air TGs, extrapolated from both on-and off-glacier sites, were applied in a distributed energy-balance model. Compared with piecewise air temperature extrapolation from all on-glacier stations, modelled ablation, using the MG-TG, increased by <1%, increasing to >4% using the environmental 'lapse rate'. Ice melt under thick debris was relatively insensitive to air temperature, while the effects of different temperature extrapolation methods were strongest at high elevation sites of thin and patchy debris cover.

There exists a need to advance our understanding of debris-covered glacier surfaces over relatively short timescales due to rapid, climatically induced areal expansion of debris cover at the global scale, and the impact debris has on mass balance. We applied unpiloted aerial vehicle structure-from-motion (UAV-SfM) and digital elevation model (DEM) differencing with debris thickness and debris stability modelling to unravel the evolution of a 0.15km 2 region of the debris-covered Miage Glacier, Italy, between June 2015 and July 2018. DEM differencing revealed widespread surface lowering (mean 4.1 ± 1.0m a-1 ; maximum 13.3m a-1). We combined elevation change data with local meteorological data and a sub-debris melt model, and used these relationships to produce high resolution, spatially distributed maps of debris thickness. These maps were differenced to explore patterns and mechanisms of debris redistribution. Median debris thicknesses ranged from 0.12 to 0.17m and were spatially variable. We observed localized debris thinning across ice cliff faces, except those which were decaying, where debris thickened. We observed pervasive debris thinning across larger, backwasting slopes, including those bordered by supraglacial streams, as well as ingestion of debris by a newly exposed englacial conduit. Debris stability mapping showed that 18.2-26.4% of the survey area was theoretically subject to debris remobilization. By linking changes in stability to changes in debris thickness, we observed that slopes that remain stable, stabilize, or remain unstable between periods almost exclusively show net debris thickening (mean 0.07m a-1) whilst those which become newly unstable exhibit both debris thinning and thickening. We observe a systematic downslope increase in the rate at which debris cover thickens which can be described as a function of the topographic position index and slope gradient. Our data provide quantifiable insights into mechanisms of debris remobilization on glacier surfaces over sub-decadal timescales, and open avenues for future research to explore glacier-scale spatiotemporal patterns of debris remobilization.

Abstract. Near-surface air temperature (Ta) is highly important for modelling glacier ablation, though its spatio-temporal variability over melting glaciers still remains largely unknown. We present a new dataset of distributed Ta for three glaciers of different size in the south-east Tibetan Plateau during two monsoon-dominated summer seasons. We compare on-glacier Ta to ambient Ta extrapolated from several local off-glacier stations. We parameterise the along-flowline sensitivity of Ta on these glaciers to changes in off-glacier temperatures (referred to as “temperature sensitivity”) and present the results in the context of available distributed on-glacier datasets around the world. Temperature sensitivity decreases rapidly up to 2000–3000 m along the down-glacier flowline distance. Beyond this distance, both the Ta on the Tibetan glaciers and global glacier datasets show little additional cooling relative to the off-glacier temperature. In general, Ta on small glaciers (with flowline distances <1000 m) is highly sensitive to temperature changes outside the glacier boundary layer. The climatology of a given region can influence the general magnitude of this temperature sensitivity, though no strong relationships are found between along-flowline temperature sensitivity and mean summer temperatures or precipitation. The terminus of some glaciers is affected by other warm-air processes that increase temperature sensitivity (such as divergent boundary layer flow, warm up-valley winds or debris/valley heating effects) which are evident only beyond ∼70 % of the total glacier flowline distance. Our results therefore suggest a strong role of local effects in modulating temperature sensitivity close to the glacier terminus, although further work is still required to explain the variability of these effects for different glaciers.

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.