In spite of the quite abundant literature focusing on fine debris deposition over glacier accumulation areas, less attention has been paid to the glacier melting surface. Accordingly, we proposed a novel method based on semi-automatic image analysis to estimate ice albedo from fine debris coverage (d). Our procedure was tested on the surface of a wide Alpine valley glacier (the Forni Glacier, Italy), in summer 2011, 2012 and 2013, acquiring parallel data sets of in situ measurements of ice albedo and high-resolution surface images. Analysis of 51 images yielded d values ranging from 0.01 to 0.63 and albedo was found to vary from 0.06 to 0.32. The estimated d values are in a linear relation with the natural logarithm of measured ice albedo (R = −0.84). The robustness of our approach in evaluating d was analyzed through five sensitivity tests, and we found that it is largely replicable. On the Forni Glacier, we also quantified a mean debris coverage rate (C_r) equal to 6 g m⁻² per day during the ablation season of 2013, thus supporting previous studies that describe ongoing darkening phenomena at Alpine debris-free glaciers surface. In addition to debris coverage, we also considered the impact of water (both from melt and rainfall) as a factor that tunes albedo: meltwater occurs during the central hours of the day, decreasing the albedo due to its lower reflectivity; instead, rainfall causes a subsequent mean daily albedo increase slightly higher than 20 %, although it is short-lasting (from 1 to 4 days).

Karakoram is one of the most glacierized region worldwide, and glaciers therein are the main water resource of Pakistan. The attention paid to this area is increasing, because the evolution of its glaciers recently depicted a situation of general stability, known as "Karakoram Anomaly", in contrast to glacier retreat worldwide. Here we focused our attention upon the glacier evolution within the Central Karakoram National Park (CKNP, a newborn park of this region, ca. 12 162 km² in area) to assess the magnitude and rate of such anomaly. By means of Remote Sensing data (i.e.: Landsat images), we analyzed a sample of more than 700 glaciers, and we found out their area change between 2001 and 2010 is not significant (+27 km² ± 42 km²), thus confirming their stationarity. We analyzed climate data, snow coverage from MODIS, and supraglacial debris presence, as well as potential (con-) causes. We found a slight decrease of summer temperatures (down to −1.5 °C during 1980–2009) and an increase of wet days during winter (up +3.3 days yr⁻¹ during 1980–2009), possibly increasing snow cover duration, consistently with MODIS data. We further detected considerable supra-glacial debris coverage (ca. 20% of the glacier area which rose up to 31% considering only the ablation area), which could have reduced buried ice melting during the last decade. These results provide further ground to uphold the existence of the Karakoram Anomaly, and present an useful template for assessment of water availability within the glaciers of the CKNP.

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r² = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.

This study provides an estimate of fresh water derived from ice melt for the ablation areas of glaciers in the Central Karakoram National Park (CKNP), Pakistan. In the CKNP there are ~700 glaciers, covering ~4600 km², with widespread debris cover (518 km²). To assess meltwater volume we applied a distributed model able to describe both debris-covered and debris-free ice ablation. The model was calibrated using data collected in the field in the CKNP area and validated by comparison with ablation data collected in the field, independent of the data used in building the model. During 23 July–9 August 2011, the mean model-estimated ablation in the CKNP was 0.024 m w.e. d^–1 in debris-covered areas and 0.037 m w.e. d^–1 in debris-free areas. We found a mean error of +0.01 m w.e. (corresponding to 2%) and a root-mean-square error equal to 0.09 m w.e. (17%). According to our model, the ablation areas of all the glaciers in the CKNP produced a water volume of 1.963 km³ during the study period. Finally, we performed several sensitivity tests for assessing the impact of the input data variations.

Glacier melt conditions (i.e., null surface temperature and positive energy budget) can be assessed by analyzing data acquired by a supraglacial automatic weather station (AWS), such as the station installed on the surface of Forni Glacier (Italian Alps). When an AWS is not present, the assessment of actual melt conditions and the evaluation of the melt amount is more difficult and simple methods based on T-index (or degree days) models are generally applied. These models require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 0 °C. In this paper, we applied both energy budget and T-index approaches with the aim of solving this issue. We start by distinguishing between the occurrence of snowmelt and the reduction in snow depth due to actual ablation (from snow depth data recorded by a sonic ranger). Then we find the daily average temperature thresholds (by analyzing temperature data acquired by an AWS on Forni Glacier) which, on the one hand, best capture the occurrence of significant snowmelt conditions and, on the other, make it possible, using the T-index, to quantify the actual snow ablation amount. Finally we investigated the applicability of the mean tropospheric lapse rate to reproduce air temperature conditions at the glacier surface starting from data acquired by weather stations located outside the glacier area. We found that the mean tropospheric lapse rate allows for a good and reliable reconstruction of glacier air temperatures and that the choice of an appropriate temperature threshold in T-index models is a very important issue. From our study, the application of the +0.5 °C temperature threshold allows for a consistent quantification of snow ablation while, instead, for detecting the beginning of the snow melting processes a suitable threshold has proven to be at least −4.6 °C.